Posterior predictive model checks for cognitive diagnostic models Online publication date: Wed, 16-Sep-2015
by Jung Yeon Park; Matthew S. Johnson; Young-Sun Lee
International Journal of Quantitative Research in Education (IJQRE), Vol. 2, No. 3/4, 2015
Abstract: Cognitive diagnostic models (CDMs; DiBello et al., 2007) have received increasing attention in educational measurement for the purpose of diagnosing examinees' strengths and weaknesses of their latent attributes. Despite the current popularity of a number of diagnostic models, research on assessing model-data fit has been limited. The current study applies one of the Bayesian model checking methods, namely the posterior predictive model check (PPMC) method (Rubin, 1984) to investigate model misfit. Specifically, we aim to employ the technique to investigate model-data misfit from various diagnostic models using real data and a simulation study. An important issue with the application of PPMC is the choice of discrepancy measure. This study examines the performance of three discrepancy measures for assessing different aspects of model fit: observed total-scores distribution, association of item pairs, and correlation of attribute pairs as adequate measures for the diagnostic models.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Quantitative Research in Education (IJQRE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook